System and method for predictive streaming

ABSTRACT

A technique for predictive streaming involves receiving a request for a block associated with a streaming application and serving data associated with the block. A block request database is checked to predict what block is likely to be requested next based upon prior block request data. The predicted block may be identified when serving the data associated with the requested block. A system developed according to the technique includes a streaming server, a block request database, and a prediction engine that uses the block request database to predict block requests. The streaming server provides data associated with the predicted block request.

CLAIM OF PRIORITY

This application claims priority to U.S. Provisional Application60/621,178, entitled SYSTEM AND METHOD FOR PREDICTIVE STREAMING, filedOct. 22, 2004, which is hereby incorporated by reference in itsentirety.

BACKGROUND

Software streaming involves downloading small pieces of files as thepieces are needed by the program being streamed. These small pieces maybe referred to as blocks. A streaming client sends requests for blocksas they are needed up to a streaming server, which sends back streamingdata that is associated with the requested block. Sending a request andreceiving the streaming data may cause delays that can slow down thestreamed program.

There are many problems associated with streaming software that it wouldbe advantageous to negate, work around, or reduce. For example,predicting blocks for streaming has not been satisfactorily addressed.

SUMMARY

A technique for predictive streaming involves receiving a request for afirst block of a streaming application, checking a block requestdatabase, predicting a second block request based on the block requestdatabase, and sending, in response to the request, data associated withthe first block and data associated with the second block. A block maybe an arbitrarily large portion of a streaming application. The blockrequest database includes probabilities or means for determiningprobabilities that the second block will be requested given that thefirst block has been requested. One or more factors may be consideredwhen determining the probability of a request for the second block. Datasent in response to the request may include data associated with thefirst block and data associated with the second block. The dataassociated with the first block and the data associated with the secondblock may not be analogous. In an embodiment, the data associated withthe first block is responsive to a block request for the first block,while the data associated with the second block is data sufficient tofacilitate making a request for the second block.

In an embodiment, the technique may further include predicting blockrequests based on the block request database, then sending dataassociated with the block requests. In another embodiment, the dataassociated with the second block includes data sufficient to render arequest for the second block unnecessary. In another embodiment, thetechnique further includes piggybacking the data associated with thesecond block on a reply to the request for the first block. In anotherembodiment, the technique further includes logging the request for thefirst block and updating the block request database to incorporate dataassociated with the logged request. In another embodiment, the techniquefurther includes setting an aggressiveness parameter, wherein the dataassociated with the second block is sent when a probability of thesecond block request is higher than the aggressiveness parameter.

A system constructed according to the technique may include a processor,a block request database that includes predictive parameters, aprediction engine that is configured to check the block request databaseand predict a second block request for a second block based upon a firstblock request for a first block and predictive parameters associatedwith the first block, and a streaming server. The streaming server maybe configured to obtain the prediction about the second block requestfrom the prediction engine, include data associated with the secondblock in a response to the first block request, in addition to dataassociated with the first block, and send the response in reply to thefirst block request.

In an embodiment, the data associated with the second block issufficient to identify the second block so as to facilitate making thesecond block request. In another embodiment, the data associated withthe second block includes data sufficient to render the second blockrequest unnecessary. In another embodiment, the streaming server isfurther configured to piggyback the data associated with the secondblock on a reply to the first block request. In another embodiment, thesystem includes a request log, wherein the streaming server is furtherconfigured to log the first block request in the request log. Theprediction engine may be further configured to update the block requestdatabase according to the request log.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a networked system for use in an exemplary embodiment;

FIG. 2 depicts a computer system for use in the system of FIG. 1;

FIG. 3 depicts a portion of the computer system of FIG. 2 and componentsof the system of FIG. 1;

FIGS. 4 and 5 depict flowcharts of exemplary methods for predictivestreaming according to embodiments;

FIG. 6 depicts a conceptual view of a system 600 for providing streamingdata and identifying a predicted block in response to a block requestaccording to an embodiment;

FIGS. 7A to 7C and 8A to 8B depict exemplary request logs and blockrequest databases according to embodiments;

FIGS. 9 to 12 depict exemplary methods according to alternativeembodiments;

FIG. 13 depicts an exemplary log request and block request databaseaccording to an embodiment.

DETAILED DESCRIPTION

Parametric predictive streaming involves maintaining parameters(parametric) to predict (predictive) which blocks will be requested byor served to a streaming client (streaming). Parametric predictivestreaming can improve pipe saturation with large reads or facilitaterapid provision of sequential small reads. Parametric predictivestreaming may, in an exemplary embodiment, also be adaptive. Parametricpredictive adaptive streaming involves changing parameters over time asaccess patterns are learned.

The following description of FIGS. 1–3 is intended to provide anoverview of computer hardware and other operating components suitablefor performing the methods of the invention described herein, but is notintended to limit the applicable environments. Similarly, the computerhardware and other operating components may be suitable as part of theapparatuses of the invention described herein. The invention can bepracticed with other computer system configurations, including hand-helddevices, multiprocessor systems, microprocessor-based or programmableconsumer electronics, network PCs, minicomputers, mainframe computers,and the like. The invention can also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network.

FIG. 1 depicts a networked system 100 that includes several computersystems coupled together through a network 102, such as the Internet.The term “Internet” as used herein refers to a network of networks whichuses certain protocols, such as the TCP/IP protocol, and possibly otherprotocols such as the hypertext transfer protocol (HTTP) for hypertextmarkup language (HTML) documents that make up the World Wide Web (theweb). The physical connections of the Internet and the protocols andcommunication procedures of the Internet are well known to those ofskill in the art.

The web server 104 is typically at least one computer system whichoperates as a server computer system and is configured to operate withthe protocols of the world wide web and is coupled to the Internet. Theweb server system 104 can be a conventional server computer system.Optionally, the web server 104 can be part of an ISP which providesaccess to the Internet for client systems. The web server 104 is showncoupled to the server computer system 106 which itself is coupled to webcontent 108, which can be considered a form of a media database. Whiletwo computer systems 104 and 106 are shown in FIG. 1, the web serversystem 104 and the server computer system 106 can be one computer systemhaving different software components providing the web serverfunctionality and the server functionality provided by the servercomputer system 106, which will be described further below.

Access to the network 102 is typically provided by Internet serviceproviders (ISPs), such as the ISPs 110 and 116. Users on client systems,such as client computer systems 112, 118, 122, and 126 obtain access tothe Internet through the ISPs 110 and 116. Access to the Internet allowsusers of the client computer systems to exchange information, receiveand send e-mails, and view documents, such as documents which have beenprepared in the HTML format. These documents are often provided by webservers, such as web server 104, which are referred to as being “on” theInternet. Often these web servers are provided by the ISPs, such as ISP110, although a computer system can be set up and connected to theInternet without that system also being an ISP.

Client computer systems 112, 118, 122, and 126 can each, with theappropriate web browsing software, view HTML pages provided by the webserver 104. The ISP 110 provides Internet connectivity to the clientcomputer system 112 through the modem interface 114, which can beconsidered part of the client computer system 112. The client computersystem can be a personal computer system, a network computer, a web TVsystem, or other computer system. While FIG. 1 shows the modem interface114 generically as a “modem,” the interface can be an analog modem, isdnmodem, cable modem, satellite transmission interface (e.g. “direct PC”),or other interface for coupling a computer system to other computersystems.

Similar to the ISP 114, the ISP 116 provides Internet connectivity forclient systems 118, 122, and 126, although as shown in FIG. 1, theconnections are not the same for these three computer systems. Clientcomputer system 118 is coupled through a modem interface 120 whileclient computer systems 122 and 126 are part of a LAN 130.

Client computer systems 122 and 126 are coupled to the LAN 130 throughnetwork interfaces 124 and 128, which can be ethernet network or othernetwork interfaces. The LAN 130 is also coupled to a gateway computersystem 132 which can provide firewall and other Internet-relatedservices for the local area network. This gateway computer system 132 iscoupled to the ISP 116 to provide Internet connectivity to the clientcomputer systems 122 and 126. The gateway computer system 132 can be aconventional server computer system.

Alternatively, a server computer system 134 can be directly coupled tothe LAN 130 through a network interface 136 to provide files 138 andother services to the clients 122 and 126, without the need to connectto the Internet through the gateway system 132.

FIG. 2 depicts a computer system 140 for use in the system 100 (FIG. 1).The computer system 140 may be a conventional computer system that canbe used as a client computer system or a server computer system or as aweb server system. Such a computer system can be used to perform many ofthe functions of an Internet service provider, such as ISP 110 (FIG. 1).The computer system 140 includes a computer 142, I/O devices 144, and adisplay device 146. The computer 142 includes a processor 148, acommunications interface 150, memory 152, display controller 154,non-volatile storage 156, and I/O controller 158. The computer system140 may be couple to or include the I/O devices 144 and display device146.

The computer 142 interfaces to external systems through thecommunications interface 150, which may include a modem or networkinterface. It will be appreciated that the communications interface 150can be considered to be part of the computer system 140 or a part of thecomputer 142. The communications interface can be an analog modem, isdnmodem, cable modem, token ring interface, satellite transmissioninterface (e.g. “direct PC”), or other interfaces for coupling acomputer system to other computer systems.

The processor 148 may be, for example, a conventional microprocessorsuch as an Intel Pentium microprocessor or Motorola power PCmicroprocessor. The memory 152 is coupled to the processor 148 by a bus160. The memory 152 can be dynamic random access memory (dram) and canalso include static ram (sram). The bus 160 couples the processor 148 tothe memory 152, also to the non-volatile storage 156, to the displaycontroller 154, and to the I/O controller 158.

The I/O devices 144 can include a keyboard, disk drives, printers, ascanner, and other input and output devices, including a mouse or otherpointing device. The display controller 154 may control in theconventional manner a display on the display device 146, which can be,for example, a cathode ray tube (CRT) or liquid crystal display (LCD).The display controller 154 and the I/O controller 158 can be implementedwith conventional well known technology.

The non-volatile storage 156 is often a magnetic hard disk, an opticaldisk, or another form of storage for large amounts of data. Some of thisdata is often written, by a direct memory access process, into memory152 during execution of software in the computer 142. One of skill inthe art will immediately recognize that the terms “machine-readablemedium” or “computer-readable medium” includes any type of storagedevice that is accessible by the processor 148 and also encompasses acarrier wave that encodes a data signal.

The computer system 140 is one example of many possible computer systemswhich have different architectures. For example, personal computersbased on an Intel microprocessor often have multiple buses, one of whichcan be an I/O bus for the peripherals and one that directly connects theprocessor 148 and the memory 152 (often referred to as a memory bus).The buses are connected together through bridge components that performany necessary translation due to differing bus protocols.

Network computers are another type of computer system that can be usedwith the present invention. Network computers do not usually include ahard disk or other mass storage, and the executable programs are loadedfrom a network connection into the memory 152 for execution by theprocessor 148. A Web TV system, which is known in the art, is alsoconsidered to be a computer system according to the present invention,but it may lack some of the features shown in FIG. 2, such as certaininput or output devices. A typical computer system will usually includeat least a processor, memory, and a bus coupling the memory to theprocessor.

In addition, the computer system 140 is controlled by operating systemsoftware which includes a file management system, such as a diskoperating system, which is part of the operating system software. Oneexample of an operating system software with its associated filemanagement system software is the family of operating systems known asWindows® from Microsoft Corporation of Redmond, Wash., and theirassociated file management systems. Another example of operating systemsoftware with its associated file management system software is theLinux operating system and its associated file management system. Thefile management system is typically stored in the non-volatile storage156 and causes the processor 148 to execute the various acts required bythe operating system to input and output data and to store data inmemory, including storing files on the non-volatile storage 156.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The present invention, in some embodiments, also relates to apparatusfor performing the operations herein. This apparatus may be speciallyconstructed for the required purposes, or it may comprise a generalpurpose computer selectively activated or reconfigured by a computerprogram stored in the computer. Such a computer program may be stored ina computer readable storage medium, such as, but is not limited to, anytype of disk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, and each coupledto a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description below.In addition, the present invention is not described with reference toany particular programming language, and various embodiments may thus beimplemented using a variety of programming languages.

FIG. 3 depicts a portion of the computer system 140 (FIG. 2) andcomponents of the system 100 (FIG. 1). FIG. 3 depicts the computersystem 140, a network 162, and a streaming client 164. The network 162could be a global information network, such as the Internet, a local orwide area network (LAN or WAN), or some other intranet or network. Forexample, the network 102 (FIG. 1) could include the network 162.Alternatively, the LAN 130 (FIG. 1) could include the network 162. Inanother alternative, the computer system 140 may by physically orwirelessly coupled to the streaming client 164.

The streaming client 164 may be coupled to and accessible through thenetwork 162. The streaming client 164 could be a software, firmware, orhardware module. Alternatively, the streaming client 164 could includesome combination of software, firmware, or hardware components. Thestreaming client 164 may be part of a computer system, such as thecomputer system 140 (FIG. 2). The streaming client 164 may include aprocessor, a memory, and a bus that couples the processor to the memory.The streaming client 164, or a computer system associated with thestreaming client 164, may make use of certain programs when executing astreaming application. For example, a streaming application may beintended for use with a specific version of DirectX™, Acrobat™, orQuickTime™, which typically is installed prior to executing thestreaming application.

The computer system 140 includes a processor 166, a memory 168, and abus 170 that couples the processor 166 to the memory 168. The memory 168may include both volatile memory, such as DRAM or SRAM, and non-volatilememory, such as magnetic or optical storage. The memory 168 may alsoinclude, for example, environment variables. The processor 166 executescode in the memory 168. The memory 168 includes a streaming server 172,a block request database 174, a prediction engine 176, and one or morestreaming applications 178. Some or all of the programs, files, or dataof the computer system 140 could be served as Web content, such as bythe server computer 106 (FIG. 1). The programs, files, or data could bepart of a server computer on a LAN or WAN, such as the server computer134 (FIG. 1).

The streaming server 172 may be configured to serve data associated witha block of a streaming application, such as one of the streamingapplications 178, in response to a request for the block. The blockrequest database 174 may include data associated with block requestsreceived from one or more streaming clients, such as the streamingclient 164. The block request database 174 may include a block requestlog, which is updated with each or with a subset of block requests. Theblock request database 174 may be client-specific or generally used byall or a subset of all streaming clients from which block requests arereceived. The block request database 174 may be application-specific orgenerally associated with all or a subset of all of the streamingapplications 178. The block request database 174 may include a blockrequest history that has been derived from data associated with blockrequests received over time, from some original default values, or fromdata input by, for example, a user or software agent that administersthe computer system 140. The block request database 174 may includeparameters derived from individual block requests, a block request log,or a block request history.

The prediction engine 176 is configured to predict one or more blocks,if any, the streaming client 164 will request. The term “engine,” asused herein, generally refers to any combination of software, firmware,hardware, or other component that is used to effect a purpose. Theprediction engine 176 may include an aggressiveness parameter (notshown). If the aggressiveness is high, the prediction engine 176 is morelikely to make a prediction than if the aggressiveness is low. In anexemplary embodiment, the aggressiveness parameter is associated with aprobability threshold. The probability threshold may be a cut-offprobability that results in predictions being made for blocks that aredetermined to have a probability of being requested that exceeds thecut-off probability. In another exemplary embodiment, the predictionengine 176 may predict any block with a probability of being requestedthat is higher than the probability threshold. In these examples, itshould be noted that the aggressiveness parameter is low (i.e., theprobability threshold is low) when aggressiveness is high. Nevertheless,for the purposes of linguistic clarity, hereinafter, it is assumed thatthe aggressiveness parameter is high when aggressiveness is high. Forexample, if the aggressiveness parameter is associated with a thresholdprobability, x, then the value of the aggressiveness parameter may bethought of as 1−x, which means the aggressiveness parameter is high whenaggressiveness is high. This is for the purposes of linguistic clarity,and should not be construed as a limitation as to how the aggressivenessparameter is implemented. It should also be noted that theaggressiveness parameter is not limited to a threshold probability.

When the prediction engine 176 makes a prediction, the streaming server172 may communicate the prediction to the streaming client 164. Theprediction may include identifying data for one or more blocks, each ofwhich met aggressiveness criteria, such as by having a predictedprobability of being requested that is higher than a probabilitythreshold. Identifying data, as used herein, is data sufficient toenable a streaming client to make a request for one or more blocks thatare associated with the identifying data. The identifying data may, forexample, include a block ID associated with a block. This identifyingdata may facilitate predictive requests for blocks before the blocks areactually needed at the streaming client 164. In an exemplary embodimentwherein the identifying data is provided to a streaming client 164, thestreaming client 164 may determine whether to request the blockassociated with streaming data. This may help prevent downloadingstreaming data that the streaming client 164 doesn't really want orneed. For example, if the streaming server 172 predicts that a streamingclient 164 will need streaming data associated with a first block, andsends streaming data associated with the first block, the streamingclient 164 is unable to choose whether to receive the streaming databased on, for example, local factors. On the other hand, if thestreaming client 164 receives identifying data, the streaming client 164may determine whether it actually wants the streaming data. In analternative embodiment, the streaming server 172 may sometimes servestreaming data associated with a predicted block right away, possiblywithout even receiving a request from the streaming client 164 for thepredicted block.

The prediction engine 176 may give a weight to a number of blocks. Forexample, the prediction engine 176 may predict a first block that ismore likely to be requested than a second block (a probabilityparameter). The streaming client 164 may first request the blocks with,for example, the highest probability of being requested. As anotherexample, the prediction engine 176 may give greater weight to a firstblock over a second block if the prediction engine 176 predicts thefirst block will be requested sooner than the second block (a temporalparameter). As another example, the prediction engine 176 may givegreater weight to a first block over a second block if the first blockis larger than the second block (a size parameter). The predictionengine 176 may weigh parameters associated with the streaming client164, as well. For example, if the streaming client 164 has a limitedbuffer size, the aggressiveness parameter may be set lower. As anotherexample, if the download bandwidth is low, the prediction engine 176 mayplace more weight on temporal or size parameters. Alternatively, thestreaming client 164 may manage block requests according to localconditions, while the prediction engine 176 acts the same for all or asubset of all streaming clients.

In operation, the streaming client 164 sends over the network 162 to thestreaming server 172 a request for a block associated with a streamingapplication of the streaming applications 178. The streaming server 172,or some other component (not shown) of the computer system 140, logs therequest. For the purposes of example only, the block request database174 is treated as a block request log. However, in various embodiments,the block request database 174 could be derived from a block request logor from individual or groups of block requests that are not recorded ina log. In an exemplary embodiment, the block request database 174includes parameters derived from previous block requests, or fromdefault or initial settings, that aid in predicting subsequent blockrequests from the streaming client 164.

The prediction engine 176 uses data from the block request database 174to predict a subsequent block request. The subsequent block request maybe for, for example, a block from the same streaming application as theinitial block request, from a DLL, from a data file, from a differentexecutable file, or from any other file or application. The streamingserver 172 serves streaming data associated with the requested block andidentifying data associated with the predicted block to the streamingclient 164. The streaming client 164 then decides whether to request theidentified predicted block. For example, the streaming client 164 mayalready have data for the block associated with the identifying data ina local cache. If the streaming server 172 sends the data again, that isa waste of bandwidth and may slow down the execution of the streamingapplication. Since the streaming server 172 sends identifying datainstead of streaming data, the streaming client 164 can, for example,check the local cache first, and request the streaming data if it is notalready available in the local cache. In another embodiment, thestreaming server 172 serves streaming data associated with the requestedblock and identifying data associated with one or more predicted blocks.The streaming client 164 then decides which of the identified blocks torequest and, for example, in what order. Naturally, if the predictionengine 176 is correct in its prediction, the streaming client 164 willeventually request a predicted block in due course even if the streamingclient 164 does not request the predicted block in response to receivingthe identifying data.

FIG. 4 depicts a flowchart of an exemplary method for predictivestreaming according to an embodiment. The flowchart starts at module 180with requesting a first block. The request may originate from astreaming client. The flowchart continues at module 182 with receivingthe request for the first block. The receipt may be at a streamingserver. The streaming server may log the request. The flowchartcontinues at module 184 with checking a block request database. Theserver may check the block request database. The block request databasemay include the logged requests. The flowchart continues at module 186with predicting a second block request based on the block requestdatabase. The server may use a prediction engine to predict the secondblock request. The prediction may be based upon block requests for thefirst block followed by block requests for the second block from astreaming client. The flowchart continues at module 188 withpiggybacking a second block ID on a reply that includes block dataassociated with the first block. The flowchart continues at module 190with receiving the first block data and the second block ID. Theflowchart continues at module 192 with determining whether to requestthe second block based on local factors. The flowchart continues atmodule 194 with requesting the second block. It is assumed for thepurposes of example that local factors merit the request for the secondblock. For example, if streaming data associated with the second blockis in a local disk cache, the second block may not be requested. Theflowchart continues at module 196 with receiving the request for thesecond block. The flowchart continues at module 198 with second a replythat includes block data associated with the second block. The flowchartends at module 200 with receiving the second block data. It should benoted that, though the flowchart may be thought of as depictingsequential small reads, as opposed to depicting building large blocks,the blocks could very well be large. This method and other methods aredepicted as serially arranged modules. However, modules of the methodsmay be reordered, or arranged for parallel execution as appropriate.

When a streaming server sends data associated with a predicted block(second block) request along with data associated with a requested block(first block), the first and second block data may be thought of as a“large block” if they are queued for sending back-to-back. However, inorder to queue the first and second blocks back-to-back, the streamingserver would have to receive the request for the second block and queuethe second block data before the streaming server was finished sendingthe first block data. When data is sent as a large block, the streamingserver can have nearly continuous output. Large blocks can help to morefully utilize available bandwidth, since large data requests can fully“saturate the pipe.” This is in contrast to sequential requests forblocks, which may not saturate the pipe because of the pause betweensending first block streaming data and receiving a request for andsending second block streaming data. Fully saturating the pipe canimprove performance.

In some cases, a block may be made large initially. For example, if astreaming application is for a level-based game, a large block mayinclude data associated with an entire level. When a request for theblock is received, data associated with the entire level is returned ascontinuous output from the streaming server. Alternatively, large blockscan be built “on the fly” based on a first block request and predictedblock requests. For example, if the streaming server sends identifyingdata for multiple predicted block requests piggybacked on a reply to afirst block request, along with streaming data associated with the firstblock, the streaming client can consecutively request some or all of themultiple predicted blocks. If the requests are made in relatively rapidsuccession, the streaming server may queue streaming data associatedwith two predicted blocks more rapidly than the streaming server sendsstreaming data associated with one predicted block. In this way, thestreaming server maintains a full queue and, accordingly, can keep thepipe saturated.

FIG. 5 depicts a flowchart of an exemplary method for predictivestreaming according to an embodiment. The flowchart starts at module 202with requesting a first block. The flowchart continues at module 204with receiving the request for the first block. The streaming server maylog the request. The flowchart continues at module 206 with predictingblock requests based on the first block request. The server may use aprediction engine to predict the block requests. The prediction enginemay make use of a block request database that includes parametersderived from, for example, prior block requests for the first block. Theflowchart continues at module 208 with sending streaming data associatedwith the first block and identifying data for the predicted blocks. Thestreaming server may piggyback the identifying data on a reply to therequest for the first block. The flowchart continues at module 210 withreceiving the streaming data and the identifying data. The streamingclient may receive the data. The flowchart continues at module 212 withrequesting one or more predicted blocks in succession. The streamingclient may determine, based upon local factors, which of the blocksassociated with the identifying data are to be requested. The streamingclient may or may not order the predictive block requests according tofactors associated with the blocks. For example, the streaming clientmay make predictive block requests in the order of probability. Theprobability for each block may be sent along with the identifying dataor derived locally. The flowchart continues at module 214 with receivingthe predictive block requests. The flowchart continues at module 216with saturating the output pipe with streaming data associated with themultiple blocks. The streaming server may saturate the output pipe byqueuing streaming data as fast as or faster than the streaming data issent to the streaming client. The streaming server may maintain outputpipe saturation even if queuing streaming data slower than streamingdata is sent to the streaming client as long as the queue remainsnon-empty. The streaming server may or may not maintain output pipesaturation for an entire streaming session. When the streaming serversaturates the pipe, it is presumed that the streaming server does notmaintain saturation throughout the entire streaming session, thoughtotal saturation may be possible. When the streaming server saturatesthe pipe, the pipe is not necessarily perfectly saturated. Streamingdata is not necessarily sent in a perfectly continuous stream. Acontinuous stream of data, as used herein, means data that is sent froma non-empty queue. If there is a period of time during which the outputqueue is empty, first and second streams of data sent before and afterthe period of time are not referred to as continuous. The flowchart endsat module 218 with receiving the streaming data. The streaming clientmay receive the streaming data. Though the streaming server saturatesthe pipe, the streaming client may or may not receive a continuousstream of data, depending on various factors that are well-understood inthe art of data transmission, such as network delays, and are,therefore, not described herein.

At times, a streaming client may rapidly request blocks in a strictlysequential manner. For example, if a sequence of blocks is associatedwith a video clip, the blocks are reasonably likely to be served, inorder, one after the other. A streaming server may recognize asequential pattern of block requests, either because the streamingserver is provided with the pattern, or because the streaming servernotices the pattern from block requests it receives over time. Thestreaming server may send the pattern to the streaming client inresponse to a block request that has been found to normally precedeblock requests for blocks that are identified in the pattern. Using thepattern, the streaming client may predictively request additional blocksin anticipation of the streaming application needing them. The streamingclient may make these requests in relatively rapid succession, sinceeach request can be made using the pattern the streaming client receivedfrom the streaming server. This may result in output pipe saturation atthe streaming server. The streaming client may or may not wait for areply to each request. Predictively requested blocks may be stored in alocal cache until they are needed. The parameters that controlrecognition of the pattern, as well as how aggressive the read-aheadschedule should be, can be independently specified at the file, fileextension, directory, and application level. In addition, it can bespecified that some blocks are predictively downloaded as soon as a fileis opened (even before seeing a read) and that the open call itselfshould wait until the initial blocks have been downloaded. In anexemplary embodiment, the pattern includes identifying data for eachblock represented in the pattern. The patterns may be included in ablock request database.

In an alternative embodiment, the streaming server may provide thestreaming client with one or more patterns as a pattern database. Thestreaming server may or may not provide the pattern database when thestreaming client first requests streaming of a streaming applicationassociated with the pattern database. The streaming client may use thepattern database to predict which blocks to request based on blockrequests it intends to make. For example, if a first block request isassociated with blocks in a pattern in the pattern database, thestreaming client requests the first block and each of the blocks in thepattern in succession. The pattern database may be included in a blockrequest database.

FIG. 6 depicts a conceptual view of a system 600 for providing blockdata and identifying a predicted block in response to a block requestaccording to an embodiment. The system 600 includes an input node 218, astreaming server 220, a request log 222, a prediction engine 224, ablock request database 226, a streaming application 228, and an outputnode 230. The input node 218 may be an interface for connecting thesystem 600 to other computer systems, or a logical node within thesystem 600 for providing block requests to the streaming server 220. Theinput node 218 may or may not be treated as part of the streaming server220. The streaming server 220 may include software, firmware, hardware,or a combination thereof. The streaming server 220 may include one ormore dedicated processors or share one or more processors (not shown)with other local or remote components. The request log 222 may be a logthat records incoming traffic, as is well-known in the art of computernetworking, or a dedicated log that only records block requests. Therequest log 222 may be associated with the streaming application 228 ormultiple streaming applications (not shown). The request log 222 may beassociated with one or more streaming clients (not shown), eitherindividually, in the aggregate, or in some combination. The request logmay be dedicated to the streaming server 220, or associated withmultiple streaming servers (not shown). The request log may be local orremote with respect to the streaming server 220. The prediction engine224 may include software, firmware, hardware, or a combination thereof.The prediction engine may include one or more dedicated processors orshare one or more processors (not shown) with other local components,such as the streaming server 220, or remote components (not shown). Theprediction engine 224 may or may not be treated as part of the streamingserver 220. The block request database 226 may include software,firmware, hardware, or a combination thereof The block request database226 may or may not be treated as part of the request log 222, part ofthe prediction engine 224, or part of the streaming server 220. In analternative embodiment, block request database 226 includes the requestlog 222. The block request database 226 includes block requestparameters. The parameters may be stored in memory as constants orvariables, act as or be environment variables. The parameters may belocally or remotely available. The parameters may be input manually,input automatically, or derived. The parameters may or may not changeover time depending upon inputs from a user or agent, block requests, orother factors. The streaming application 228 is any application that isavailable for streaming. The streaming application 228 may or may not beprepared for streaming in advance, such as is disclosed, for example, inthe U.S. patent application Ser. No. 10/926,635, filed Aug. 25, 2004,entitled, INTERCEPTION-BASED RESOURCE DETECTION SYSTEM, which isincorporated herein in its entirety for all purposes. While only thestreaming application 228 is illustrated in FIG. 6, multiple streamingapplications could be included in the system 600. The multiple streamingapplications could be discretely managed (e.g., by allowing onlypredictions of blocks from the same streaming application as a requestedblock) or as a collection of blocks (e.g., a predicted block could befrom a streaming application that is different from that of therequested block). The streaming applications could be local or remote.The output node 230 may be an interface for connecting the system 600 toother computer systems, or a logical node within the system 600 forsending block requests from the streaming server 220. The output node230 may or may not be treated as part of the streaming server 220. Theoutput node 230 and the input node 218 may or may not be treated ascomponents of an input/output node.

In operation, the input node 219 receives a block request from astreaming client (not shown). The input node 219 provides the blockrequest to the streaming server 220. The streaming server 220 logs theblock request in the request log 222. The streaming server obtains aprediction from the prediction engine 224. The logging of the requestand the obtaining of a prediction need not occur in any particularorder. For example, the streaming server 220 could obtain a predictionfrom the prediction engine 224 prior to (or without consideration of)the logged block request.

The prediction engine 224 checks the request log 222, performscalculations to represent data in the request log 222 parametrically,and updates the parameters of the block request database 226. Theprediction engine 224 checks the parameters of the block requestdatabase 226 in order to make a prediction as to subsequent blockrequests from the streaming client. The checking of the request log andchecking of the parameters need not occur in any particular order. Forexample, the prediction engine 224 could check the parameters prior tochecking the request log and updating the parameters. The predictionengine 224 could check the request log and update the parameters as partof a routine updating procedure, when instructed to update parameters bya user or agent of the system 600, or in response to some otherstimulus, such as an access to the streaming application 228.Accordingly, the checking of the log request and updating of theparameters may be thought of as a separate, and only indirectly related,procedure vis-à-vis the checking of parameters to make a prediction. Theprediction may be in the form of one or more block IDs, identifying datafor one or more blocks, a pattern, or any other data that can be used bya streaming client to determine what blocks should be requestedpredictively.

When the streaming server 220 obtains the prediction, the streamingserver 220 optionally accesses the requested block of the streamingapplication 228. Obtaining the prediction and accessing the requestedblock need not occur in any particular order and could overlap. Theprediction provided to the streaming server 220 may or may not bemodified by the streaming server 220. For example, the prediction mayinclude data that is used to “look up” identifying data. In this case,the portion of the streaming server 220 that is used to look upidentifying data may be referred to as part of the prediction engine224.

Access to the requested block of the streaming application 228 isoptional because the streaming server 220 could simply return anidentifier of the predicted block without sending the predicted blockitself. Indeed, it may be more desirable to send only a predictionbecause the recipient of the prediction (e.g., a streaming client) mayalready have received the block. If the recipient already received theblock, and the block remains cached, there is probably no reason to sendthe block again. Accordingly, the streaming server, upon receiving theprediction, would not request the predicted block again. The streamingserver 220 can be referred to as obtaining identifying data from theprediction engine 224, where the identifying data can be used by, forexample, the streaming client when making one or more predictive blockrequests.

The streaming server 220 provides data associated with the requestedblock, such as streaming data, and the prediction, such as identifyingdata, to the output node 230. The data is sent from the output node 230to, for example, a streaming client (not shown). In an exemplaryembodiment, the prediction is piggy-backed on the reply that includesthe, for example, streaming data. In another exemplary embodiment, theprediction could be sent separately, either before, at approximately thesame time as, or after the reply that includes the, for example,streaming data. In an alternative embodiment, the streaming server 220could access a predicted block of the streaming application 228 and senda reply that includes streaming data associated with the predicted blockas part of the reply that includes the requested block data. Or, thestreaming server 220 could send streaming data associated with thepredicted block separately, before, at the same time as, or aftersending the reply that includes the requested block data.

A prediction may or may not always be provided by the prediction engine224. If no prediction is provided to the streaming server 220, thestreaming server may simply provide streaming data associated with therequested block. In an exemplary embodiment, the prediction engine 224may fail to provide a prediction if it does not have sufficient data tomake a prediction. Alternatively, the prediction engine 224 may providea prediction only if it meets a certain probability. For example, anaggressiveness parameter may be set to a cut-off threshold of, e.g.,0.5. If predictive certainty for a block does not meet or exceed thecut-off threshold, the prediction engine 224 may not provide theprediction for the block to the streaming server 220.

FIGS. 7A to 7C are intended to help illustrate how to predict a blockrequest based on the requested block. FIGS. 7A to 7C depict exemplaryblock request logs and parameters derived therefrom according toembodiments. FIGS. 7A to 7C are not intended to illustrate preferredembodiments because there are many different items of data that could berecorded or omitted in the request log and many different parametersthat could be derived from selected items of data.

FIG. 7A depicts an exemplary request log 232 and a block requestdatabase 234. In this example, the request log 232 includes a listing ofblocks in the order the blocks were requested from a streaming client.For exemplary purposes, the request log 232 is assumed to be associatedwith a single streaming client. The request log 232 may or may not beassociated with a single streaming application. The block requestdatabase 234 includes parameters derived from the request log 232. Forexemplary purposes, the parameters are derived only from entries in therequest log 232. In alternative embodiments, the parameters couldinclude default values or otherwise rely on data that is not included inthe request log 232. For the purposes of example, the request log 232 isassumed to include only the values shown in FIG. 7A. For the purposes ofexample, a block request (the current block request) is considered foreach of the blocks 3 to 8.

Current block request for block 3: As illustrated in the request log232, a block request for block 5 (the second block request) immediatelyfollows the request for block 3 (the first block request). Since thesecond block request follows the request for block 3, a prediction canbe made about whether the current block request (for block 3) will befollowed by a request for block 5. Since, for the purposes of example,the parameters are derived only from the request log 232 (and no otherdata is considered), it might be assumed that a request for block 5 is100% certain. The predicted block parameters array for block 3 is [(5,1.0)]. This can be interpreted to mean, following a request for block 3,the probability of a request for block 5 is 1.0. Of course, this isbased on a small data sample and is, therefore, subject to a very largeerror. However, over time the probability may become more accurate.

Current block request for block 4: As illustrated in the request log232, a block request for block 4 has not been made. Accordingly, noprediction can be made.

Current block request for block 5: For reasons similar to those givenwith respect to the request for block 3, the predicted parameters arrayfor block 5 is [(8, 1.0)].

Current block request for block 6: As illustrated in the request log232, a block request for block 6 has been made before, but it was thelast block requested (none follow). Accordingly, no prediction can bemade.

Current block request for block 7: For reasons similar to those givenwith respect to the request for block 3, the predicted parameters arrayfor block 5 is [(8, 1.0)].

Current block request for block 8: As illustrated in the request log232, a block request for block 8 has been made twice before. The blockrequest following a request for block 8 was 3 one time and 6 the othertime. It may be assumed, for exemplary purposes, that the request forblock 3 or 6 is equally probable. Accordingly, the predicted blockparameters array for block 8 is [(3, 0.5), (6, 0.5)]. This can beinterpreted to mean, following a request for block 8, the probability ofa request for block 3 is 0.5 and the probability of a request for block6 is 0.5. Since the requests for blocks 3 and 6 are considered equallyprobable, data associated with both block 3 and block 6 may be includedin a reply. Alternatively, neither may be used. Also, an aggressivenessparameter may be set that requires a higher than 0.5 probability inorder to be predicted.

Other factors could be considered in “breaking a tie” between equallyprobable block predictions, such as blocks 3 and 6, which are predictedto follow block 8 in FIG. 7A. A higher-order predictor (e.g., afirst-order predictor, as described later) could break the tie dependingon whether block 7 was requested prior to block 8 (implying that block 3is the better choice), or block 5 was requested prior to block 8(implying that block 6 is the better choice). The returned list ofpredicted blocks may be constructed using all n-order predictors, andthe best N predicted blocks, regardless of which n-order predictor itcame from, may be returned in ranked order. If, for example, thefirst-order predictor gave a probability of 1.0 to predicted block 3,but the zero-order predictor gave a probability of 0.5, then block 3would be included with a probability of 1.0, the max of theprobabilities for that particular block.

In this example, a prediction was made as to which block was requestedfollowing a current block request, though in alternative examples,predictions could be made as to the next requested block, the one after,any other subsequent block request, or some combination thereof. Also,the predicted block parameters are represented in an array, but any datastructure that captures the relevant information would be acceptable.

FIG. 7B depicts an exemplary request log 236 and a block requestdatabase 238 according to another embodiment. In this example, therequest log 236 includes a listing of blocks in the order the blockswere requested from a streaming client, plus a time field thatrepresents what time the blocks were sent by a streaming client. In analternative, the time field may represent what time the blocks werereceived, logged, or otherwise managed. The block request database 238includes a predicted block parameters array with a time field, whichrepresents the time difference between when a first block request and asecond block request were sent. For example, for the predicted blockparameters array for block 3, the time field is 6:18:04, which isintended to mean 6 hours, 18 minutes, and 4 seconds. This is thedifference between when block 3 and block 5 were sent, as shown in therequest log 236. As another example, the predicted block parametersarray for block 8 is [(3, 0.5, 0:05:20), (6, 0.5, 5:10:15)]. This can beinterpreted to mean that blocks 3 and 6 are equally likely to berequested following a request for block 8. However, since there is atime entry, weight can be given to the entry with the lower associatedtime difference. Since, with respect to a streaming application, 5 hoursis a long time, weight may be given to block 3 (requested about 5minutes after block 8) over block 6 (requested about 5 hours after block8). Indeed, in this example, since the time between block 6 and block 8is so long, a prediction engine could choose to assume that there reallyisn't a predictive relationship between blocks 6 and 8.

In an embodiment, time-related predictive parameters may be ignored ifthey are too high. The threshold value over which a time differencewould result in a block request being ignored may be referred to as atemporal aggressiveness parameter. For example, if a temporalaggressiveness parameter is 1 hour, then the predicted block parametersarray for block 8 could be rewritten as [(3, 1.0, 0:05:20)]. That is,the values related to block 6 are ignored since block 6 was receivedmore than 1 hour after block 8, according to the request log 236.Alternatively, the predicted block parameters array for block 8 could berewritten as [(3, 0.5, 0:05:20)], which is basically the same, but theprobability is not recalculated when the values related to block 6 areignored. Similarly, the predicted block parameters array for block 3could be rewritten as [ ], since there are no block requests within 1hour (the temporal aggressiveness threshold, in this example) of theblock request for block 3.

In an alternative embodiment, a prediction engine could keep chainingpredicted blocks back through the prediction engine to get subsequentpredicted blocks, under the assumption that the “first round” predictedblocks were correct. The probabilities of subsequent predicted blocksmay be multiplied by the probability of the original predicted block toaccurately get a predicted probability for the secondary (ternary, etc)blocks. This could continue until the probabilities fell below anaggressiveness threshold. A limit may be placed on the max number ofpredictions returned. It should be noted that in the case of a longchain of blocks that follow each other with probability 1.0, the firstrequest may return a list of all subsequent blocks in the chain, which astreaming client can then “blast request” to keep the pipeline at thestreaming server full.

FIG. 7C depicts an exemplary request log 240 and a block requestdatabase 242 according to another embodiment. FIG. 7C is similar to FIG.7B, but entries in the request log 240 include a streaming clientassociated with a block request. In this example, the streaming clientis assumed to have requested the block with which the streaming clientis associated. For the purposes of determining the predicted blockparameters, an entry for a given streaming client, for example client 1,is compared to other entries of the streaming client. It may make nodifference whether, for example, client 2 requests a block some timeafter client 1. However, it may make a difference if client 1 requestsblock 3 after requesting block 8 and client 2 requests block 3 afterrequesting block 8. Using this principle, predictive parameters canadapt over time based on the block requests of various clients. Theprinciples for calculating predictive parameters is the same asdescribed with reference to FIG. 7B, but predictive accuracy can beimproved by considering the block requests of multiple clients, as shownin the block request database 242 of FIG. 7C.

In an embodiment, the times are not actually recorded, as depicted inFIG. 7C. Rather, the times are used as a filter to decide whether oneblock truly follows another block in terms of predictive value. In suchan embodiment, the entry for block 3 may be more similar to the exampleentry 242-1, which has a value of [(4, 1.0, 0:07:05)], than is depictedfor illustrative purposes, in the block request database 242. In otherwords, in this alternative, the block request database 242 does notinclude any predictive data for block 5. Similarly, the entry for block4 may be the empty set, and the entry for block 8 may be similar to theexample entry 242-2. This alternative, where the probabilities areomitted for rejected blocks, may be more desirable since it omits datathat is probably irrelevant for predictive purposes.

FIGS. 8A and 8B depict request logs and block request databases for usewith zero order and first order predictions. In an exemplary embodiment,a streaming server builds up the zero, first, or higher orderprobabilities over time based on which blocks have been requested whilestreaming an application. FIG. 8A depicts a request log 244 and a blockrequest database 246. Each block associated with a streaming applicationhas a probability of being requested when streaming the streamingapplication that is based on how many times the block is requested whenstreaming the streaming application, compared to the total number oftimes the streaming application has been streamed. For the purposes ofexample, one instance, from beginning to end, of streaming a streamingapplication may be referred to as a session. In a given session, one ormore blocks, in one or more combinations, may be requested.

A session may be thought of in terms of the block requests made over thecourse of streaming a streaming application. For example, in the fivesessions depicted in the log request 244 of FIG. 8A, Session 1 consistsof three block requests: 7, 8, and 3; Session 2 consists of three blockrequests: 8, 3, and 4; Session 3 consists of three block requests: 5, 8,and 6; Session 4 consists of two block requests: 8 and 3; and Session 5consists of two block requests: 8and 3. Considering only these fivesessions, a probability associated with a block request may bedetermined by adding the number of times a block is requested in asession and dividing the sum by the total number of sessions (in thiscase, five). For example, as shown in the block request database 246,Block 3, which is included in Sessions 1, 2, 4, and 5, has a zero orderprobability of 0.8. Blocks 4, 5, 6, and 7 each have a zero orderprobability of 0.2 because they are included only in Session 2, 3, 3,and 1, respectively. Since Block 8 is included in each of the Sessions1–5, Block 8 has a zero order probability of 1.0. In an embodiment,probabilities are constructed over multiple sessions by multiple usersto obtain composite probabilities.

In some embodiments, it may be desirable to utilize a higher orderprobability. For example, if the streaming application is a game programthat starts in a room with four doors that lead to four different rooms,each of which is associated with multiple block requests, the multipleblocks associated with each of the four different rooms may be equallylikely (about 25% each). If a door is selected, each of the multipleblocks associated with each of the doors (even those not taken) may beabout 25%. By using a first-order predictor, once we see the first blockrequest for one of the rooms, the subsequent blocks for that room willhave first-order probabilities near 100%, and so the multiple blocks forthat room can be predictively downloaded.

FIG. 8B is intended to illustrate first order probabilities. FIG. 8Bdepicts a request log 248 and a block request database 250. Whendetermining first order probabilities, a first block request is used ascontext. In other words, if a first block request is made, theprobability of a second block request may be calculated by comparing theprobability, based upon previous sessions, that the second block requestfollows (or occurs during the same session as) the first block request.Since the probability of the second block request is based on one blockof context, the probability may be referred to as a first orderprobability. First-order predictions may or may not be more accuratethan zero-order predictions, and both zero- and first-orderprobabilities may be used, individually or in combination, when making ablock request prediction.

Given the request log 248, the probability that a second block will berequested following a first block request can be determined for one ormore sessions. The block request database 250 is organized to show thefirst-order probability for a block, given the context of another block.The block request database 250 is not intended to illustrate a datastructure, but rather to simply illustrate, for exemplary purposes,first-order probability. The probability of a block request (for theblock in the Block column) is depicted under the first-order probabilityfor each block. For example, the first-order probability of a blockrequest for Block 3 is 1.0 with Block 4 as context, 0 with Block 5 ascontext, 0 with Block 6 as context, 1.0 with Block 7 as context, and 0.8with Block 8 as context. These probabilities are derived as follows. Asis shown in the request log 248, when blocks 5 or 6 are requested(Session 3), block 4 is not requested. Accordingly, the first-orderprobability for block 3 with either block 5 or 6 as context is 0. Whenblocks 4 or 7 have been requested (Sessions 2 and 1, respectively),block 3 is also requested. Accordingly, the first-order probability forblock 3 with block 4 as context is 1.0. When block 8 has been requested(all five sessions), block 3 is also requested 4 out of 5 times.Accordingly, the first-order probability for block 3 with block 8 ascontext is 0.8. First-order probabilities for each block can be derivedin a similar manner. The probabilities are shown in the block requestdatabase 250.

Second- and higher-order probabilities can be determined in a similarmanner to that described with reference to FIG. 8B, but with multipleblock requests as context.

In an exemplary embodiment, a streaming server can use the zero- orhigher order probabilities to determine which blocks have probabilitiesthat exceed a “predictive download aggressiveness” parameter andpiggyback those block IDs, but not the block data itself, onto the blockdata returned to a streaming client. The client can then decide whichblocks it will predictively download, based on local factors. Localfactors may include client system load, contents of the local diskcache, bandwidth, memory, or other factors.

It should be noted that in certain embodiments, it may not be possibleto incorporate all incoming block requests into the request log becauseblock requests are being “filtered” by the local disk cache. This mayresult in the request log including only those block requests for blocksthat weren't in the local disk cache, which will throw the predictorsoff. Accordingly, in an embodiment, a streaming server may indicate to astreaming client that the server is interested in collecting predictiondata. In this case, the streaming client would send a separate datastream with complete block request statistics to the streaming server.In this example, the separate data stream may include all actual blockrequests made by the application, regardless of whether that request wassuccessfully predicted or is stored in a local cache of the streamingclient.

FIG. 9 depicts a flowchart of an exemplary method for updatingpredictive parameters. The flowchart starts at module 252 with informinga streaming client of an interest in collecting prediction data. Thisrequest may or may not be included in a token file. The request may besent to a streaming client when the streaming client requests streamingof an application. After module 252, the flowchart continues along twopaths (254-1 and 254-2). The modules 254 may occur simultaneously, orone may occur before the other.

The flowchart continues at module 254-1 with receiving block requests.The streaming server may receive the block requests from the streamingclient. The flowchart continues at module 256-1 with sending dataassociated with the block requests, including predictions, if any. Theflowchart continues at decision point 258-1, where it is determinedwhether the session is over. If the session is not over, the flowchartcontinues from module 254-1 for another block request. Otherwise, if thesession is over, the flowchart ends for modules 254-1 to 258-1.

The flowchart continues at module 254-2 with receiving block requeststatistics, including block requests for blocks stored in a local diskcache. Module 254-2 may or may not begin after module 258-1 ends. Module254-2 may or may not continue after module 258-1 ends. The flowchartcontinues at module 256-2 with updating predictive parameters using theblock request statistics. The predictive parameters may be used atmodule 256-1 to provide predictions. The flowchart continues at decisionpoint 258-2, where it is determined whether the session is over. If thesession is not over, the flowchart continues from module 254-2 for moreblock request statistics. Otherwise, if the session is over, theflowchart ends for modules 254-2 to 258-2.

In another embodiment, a streaming client may indicate to a streamingserver that the streaming client is interested in receiving predictiveblock data IDs. Then the streaming server would piggyback the IDs, asdescribed previously. FIG. 10 depicts a flowchart of an exemplary methodfor providing predictions to a streaming client. The flowchart begins atmodule 260 with receiving notice that a streaming client is interestedin receiving predictive block data IDs. The streaming client may sendthe notice when requesting streaming of an application from a streamingserver. The flowchart continues at module 262 with receiving a blockrequest. The streaming server may receive the block request from thestreaming client. The flowchart continues at decision point 264 where itis determined whether a prediction is available. If a prediction isavailable (264-Y), then the flowchart continues at module 266 withpiggybacking one or more predicted block IDs on a reply to the blockrequest, and at module 268 with sending the reply to the block request.If, on the other hand, a prediction is not available (264-N), then theflowchart continues at module 268 with sending the reply to the blockrequest (with no predicted block ID). In either case, the flowchartcontinues at decision point 270, where it is determined whether thesession is over. If the session is not over (270-N), then the flowchartcontinues at module 262, as described previously. If, on the other hand,the session is over (270-Y), then the flowchart ends.

In another embodiment, the streaming server may send the block requestdatabase to the client along with an initial token file so that theclient can do all of the predictive calculations. FIG. 11 depicts aflowchart of an exemplary method for providing predictive capabilitiesto a streaming client. The flowchart begins at module 272 with sending ablock request database to a streaming client. The streaming client mayor may not have requested the block request database from a streamingserver. The block request database may be sent in response to a requestfor streaming of an application. The flowchart continues at module 274with receiving block requests, including predictive block requests, fromthe streaming client. Since the streaming client has the block requestdatabase, the streaming client is able to make predictions about whichblocks it should request in advance. The streaming server need notpiggyback IDs in this case, since the streaming client makes thedecision and requests the blocks directly. The flowchart ends at module276 with sending data associated with the block requests to thestreaming client. The streaming client may or may not send an alternatedata stream with actual block request patterns to the streaming server.Modules 274 and 276 may occur intermittently or simultaneously.

In another embodiment, the streaming client may collect predictive datalocally and then send the data to the streaming server at the end of thesession. FIG. 12 depicts a flowchart of an exemplary method forreceiving predictive data from a streaming client. The flowchart beginsat module 278 with receiving block requests. The block requests may befrom a streaming client. The flowchart continues at module 280 withsending block requests, including predictions, if any, to the streamingclient. The flowchart continues at decision point 282, where it isdetermined whether the session is over. If the session is not over(282-N), then the flowchart continues at module 278, as describedpreviously. If, on the other hand, the session is over (282-Y), then theflowchart continues at module 284 with receiving predictive data fromthe streaming client. The predictive data may include a request log, ablock request history, or one or more parameters derived from blockrequest data. The flowchart ends at module 286 with updating a blockrequest database using the predictive data. In this way, the streamingserver can adapt the block request database in response to each session.

In an alternative embodiment, a streaming server can maintain a blockrequest database that keeps track of “runs.” Runs are sets of blocks forwhich the block requests occur closely spaced in time. For example, if asequence of blocks is requested within, say, 0.5 second of a precedingblock request in the sequence, the sequence of blocks may be referred toas a run. Runs can be used to efficiently utilize memory resources byrecording probabilities on the run level instead of per block. Runs canalso be used to reduce the amount of predictive download data. Forexample, for a level-based game, a user may download a first block, thenthe rest of the game will be predictively downloaded for each level,since the probabilities of downloading each level are, for the purposesof this example, nearly 100%.

By keeping track of runs, after the first level has been downloaded,predictive downloads can be “shut off” until the start of blocks for thesecond level begin. For example, if the blocks at the beginning of therun, which trigger or signal the run, are detected, the block IDs forthe rest of the run may be sent to the streaming client, who then chainrequests them. However, those subsequent block requests do not triggerany further run. So, the predictive downloads cause the blocks in themiddle of a run to not act as a triggering prefix of another run; nofurther predictions are made from the middle of a run.

FIG. 13 is intended to illustrate maintaining a block request databasethat includes runs. FIG. 13 depicts a request log 288 and a blockrequest database 290. Parameters associated with blocks and runs areomitted from the block request database 290 so as to more clearly focuson the point being illustrated. The omitted parameters could be anyparameters derived from the request log for the purpose of facilitatingthe prediction of future block requests (e.g., first-orderprobabilities). The request log 288 includes two sessions, Session 1 andSession 2. In Session 1, a series of blocks are received in succession.For the purposes of example, a run parameter (not shown) is set to onesecond. If blocks are received within one second of one another, theymay be considered part of a run. In the example of FIG. 13, the blocks17–25 are received within one second of one another, and the blocks 15and 16 are received more than one second from each of the blocks 17–25.When parameters are derived for inclusion in the block request database290, the blocks 17–25, which are a run, can be grouped together, asillustrated in block request database 290-1. In Session 2, a differentseries of blocks are received in succession. The blocks 17–19 and 26–30are received within one second of one another so, in this example, theycan be considered a run. The run can be grouped together, as illustratedin block request database 290-2. When the block request database 290-1and block request database 290-2 are combined, since the run 17–25 is nolonger certain (i.e., an alternative run could be 17–19, 26–30), the runmust be broken into two different runs, as depicted in the block requestdatabase 290. In this way, fewer data entries (one for each requestedblock) are required, since blocks can be grouped as a run.

When a run is stored in the block request database, each block of therun may be downloaded in succession without requiring individualpredictions. In an alternative embodiment, each successive block of arun may be given an effective probability of 1.0, which guaranteesfavorable predictive treatment regardless of the aggressivenessthreshold (assuming the threshold allows for some prediction). In yetanother alternative, a streaming client may receive an identifier forthe run and request successive blocks in the run, using the identifierto identify the successive blocks.

While this invention has been described in terms of certain embodiments,it will be appreciated by those skilled in the art that certainmodifications, permutations and equivalents thereof are within theinventive scope of the present invention. It is therefore intended thatthe following appended claims include all such modifications,permutations and equivalents as fall within the true spirit and scope ofthe present invention; the invention is limited only by the claims.

1. A method, comprising: receiving a request for a first block of astreaming application; checking a block request database; predicting asecond block request based on the block request database; sending, inresponse to the request, data associated with the first block and dataassociated with the second block; logging the request for the firstblock; updating the block request database to incorporate dataassociated with the logged request; wherein the logged request is afirst logged request, setting a temporal aggressiveness parameter,wherein the block request database is updated to incorporate dataassociated with the first logged request to the extent that associationswith a second logged request is made if a difference between a receivetime associated with the first logged request and the second loggedrequest is less than the temporal aggressiveness parameter.
 2. Themethod of claim 1, further comprising predicting a plurality of blockrequests based on the block request database, wherein said sendingfurther includes sending data associated with the plurality of blockrequests.
 3. The method of claim 1, wherein the data associated with thesecond block is sufficient to identify the second block so as tofacilitate making a request for the second block.
 4. The method of claim1, wherein the data associated with the second block includes datasufficient to render a request for the second block unnecessary.
 5. Themethod of claim 1, further comprising piggybacking the data associatedwith the second block on a reply to the request for the first block. 6.The method of claim 1, further comprising setting an aggressivenessparameter, wherein the data associated with the second block is sentwhen a probability of the second block request is higher than theaggressiveness parameter.
 7. A system comprising: a means for receivinga request for a first block of a streaming application; a means forchecking a block request database; a means for predicting a second blockrequest based on the block request database; a means for sending, inresponse to the request, data associated with the first block and dataassociated with the second block; a means for logging the request forthe first block; a means for updating the block request database toincorporate data associated with the logged request; wherein the loggedrequest is a first logged request, a means for setting a temporalaggressiveness parameter, wherein the block request database is updatedto incorporate data associated with the first logged request to theextent that associations with a second logged request is made if adifference between a receive time associated with the first loggedrequest and the second logged request is less than the temporalaggressiveness parameter.
 8. The system of claim 7, wherein the dataassociated with the second block is sufficient to identify the secondblock so as to facilitate making a request for the second block.
 9. Thesystem of claim 7, wherein the data associated with the second blockincludes data sufficient to render a request for the second blockunnecessary.
 10. The system of claim 7, further comprising a means forpiggybacking the data associated with the second block on a reply to therequest for the first block.
 11. A system comprising: a processor; ablock request database that includes predictive parameters; a predictionengine, coupled to the processor and the block request database, that isconfigured to check the block request database and predict a secondblock request for a second block based upon a first block request for afirst block and predictive parameters associated with the first block; astreaming server, coupled to the prediction engine, that is configuredto: obtain the prediction about the second block request from theprediction engine, include data associated with the second block in aresponse to the first block request, in addition to data associated withthe first block, and send the response in reply to the first blockrequest; a request log, wherein the streaming server is furtherconfigured to log the first block request in the request log, whereinthe prediction engine is further configured to update the block requestdatabase according to the request log; a temporal aggressivenessparameter embodied in a computer readable storage medium; wherein thelogged request is a first logged request, wherein the block requestdatabase is updated to incorporate data associated with the first loggedrequest to the extent that associations with a second logged request ismade if a difference between a receive time associated with the firstlogged request and the second logged request is less than the temporalaggressiveness parameter.
 12. The system of claim 11, wherein the dataassociated with the second block is sufficient to identify the secondblock so as to facilitate making the second block request.
 13. Thesystem of claim 11, wherein the data associated with the second blockincludes data sufficient to render the second block request unnecessary.14. The system of claim 11, wherein the streaming server is furtherconfigured to piggyback the data associated with the second block on areply to the first block request.